Domain Generalization FAS Methods. In the face anti-spoofing community, domain generalization techniques are developed to address the domain shift between different anti-spoofing datasets. Shao  [1]} employed meta-learning techniques to simulate the target domain shift during the training process to regularize the feature learning directions. Wang  [2]} proposed to learn domain-independent features via a disentangled representation learning framework. The most related work to ours is  [3]}, which treats live and spoof samples asymmetrically and applies adversarial loss and triplet loss to regularize the features in the normalized space. Actually, domains are hard to define in FAS tasks, as even within the same dataset, there are captures with very different capture devices. While people are using generalization methods to find common features across collections and spoof types, we aim to break the concept of domain and propose to learn a generic embedding space that encodes capture characteristics explicitly.
